from sklearn import datasets,svm
import pandas as pd
from sklearn import metrics
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier

iris=datasets.load_iris()
iris_data=iris['data']
iris_target=iris['target']

# X_train,X_test,y_test=train_test_split(iris_data,iris_target,test_size=0.25,random_state=0)
# clf=svm.SVC(C=0.8,kernel='rbf',gamma=20,decision_function_shape='ovr')
# model=clf.fit(X=train,y_train)
# y_pred=model.predict(X_test)
# print('ACC:%.4f'%metrics.accuracy_score(y_test,y_pred))
# print(metrics.classification_report(y_test,y_pred))

biopsy=datasets.load_breadt_cancer()
X=biopsy['data']
Y=biopsy['target']
x_train,x_test,y_test=train_test_split(X,Y,random_state=21)

# clf=DecisionTreeClassifier(random_state=21)
# clf.fit(x_train,y_train)
# y_test_pred=clf.predict(x_test)
# accutacy=np.mean(y_test_pred=y_test)
# print(accuracy)

from sklearn.ensemble import

